





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase_							=				{
    """configuration_speech_to_text""": ["""SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Speech2TextConfig"""],
    """processing_speech_to_text""": ["""Speech2TextProcessor"""],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_							=				["""Speech2TextTokenizer"""]

try:
							if not is_speech_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_							=				["""Speech2TextFeatureExtractor"""]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_							=				[
							    """TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFSpeech2TextForConditionalGeneration""",
							    """TFSpeech2TextModel""",
							    """TFSpeech2TextPreTrainedModel""",
							]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_							=				[
							    """SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """Speech2TextForConditionalGeneration""",
							    """Speech2TextModel""",
							    """Speech2TextPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
							from .processing_speech_to_text import SpeechaTextProcessor

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_speech_to_text import SpeechaTextTokenizer

							try:
														if not is_speech_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_speech_to_text import (
														    TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFSpeechaTextForConditionalGeneration,
														    TFSpeechaTextModel,
														    TFSpeechaTextPreTrainedModel,
														)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_speech_to_text import (
														    SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SpeechaTextForConditionalGeneration,
														    SpeechaTextModel,
														    SpeechaTextPreTrainedModel,
														)

else:
							import sys

							UpperCAmelCase_							=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
"""simple docstring"""



import os

import pytest
from attr import dataclass


SCREAMING_SNAKE_CASE__:List[str]									=			"""us-east-1"""  # defaults region






@dataclass
class  snake_case__      :
 _snake_case       :					str
 _snake_case       :					Optional[Any]							  =						"""arn:aws:iam::558105141721:role/sagemaker_execution_role"""
 _snake_case       :					Optional[Any]							  =						{
     """task_name""": """mnli""",
     """per_device_train_batch_size""": 16,
     """per_device_eval_batch_size""": 16,
     """do_train""": True,
     """do_eval""": True,
     """do_predict""": True,
     """output_dir""": """/opt/ml/model""",
     """overwrite_output_dir""": True,
     """max_steps""": 500,
     """save_steps""": 5_500,
 }
 _snake_case       :					List[str]							  =						{**hyperparameters, """max_steps""": 1_000}


 @property
 def 						a__   ( self						):
        if self.framework == "pytorch":
               return [
                   {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                   {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
                   {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
               ]
        else:
               return [
                   {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                   {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
                   {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
               ]


 @property
 def 						a__   ( self						):
        return F"{self.framework}-transfromers-test"


 @property
 def 						a__   ( self						):
        return F"./tests/sagemaker/scripts/{self.framework}"






 @property
 def 						a__   ( self						):
        if self.framework == "pytorch":
               return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
        else:
               return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"



@pytest.fixture(scope="class"				)
def 	_lowerCamelCase(	a				):
       __a       =    SageMakerTestEnvironment(framework=request.cls.framework				)
